BGElasor: Elastic-Scaling Framework for Distributed Streaming Processing with Deep Neural Network - Network and Parallel Computing Access content directly
Conference Papers Year : 2019

BGElasor: Elastic-Scaling Framework for Distributed Streaming Processing with Deep Neural Network

Abstract

In face of constant fluctuations and sudden bursts of data stream, elasticity of distributed stream processing system has become increasingly important. The proactive policy offers a powerful means to realize the effective elastic scaling. The existing methods lack the latent features of data stream, it leads the poor prediction. Furthermore, the poor prediction results in the high cost of adaptation and the instability. To address these issues, we propose the framework named BGElasor, which is a proactive and low-cost elastic-scaling framework based on the accurate prediction using deep neural networks. It can capture the potentially-complicated pattern to enhance the accuracy of prediction, reduce the cost of adaptation and avoid adaptation bumps. The experimental results show that BGElasor not only improves the prediction accuracy with three kinds of typical loads, but also ensure the end-to-end latency on QoS with low cost.
Fichier principal
Vignette du fichier
486810_1_En_10_Chapter.pdf (622.46 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03770524 , version 1 (06-09-2022)

Licence

Attribution

Identifiers

Cite

Weimin Mu, Zongze Jin, Weiping Wang, Weilin Zhu, Weiping Wang. BGElasor: Elastic-Scaling Framework for Distributed Streaming Processing with Deep Neural Network. 16th IFIP International Conference on Network and Parallel Computing (NPC), Aug 2019, Hohhot, China. pp.120-131, ⟨10.1007/978-3-030-30709-7_10⟩. ⟨hal-03770524⟩
5 View
26 Download

Altmetric

Share

Gmail Facebook X LinkedIn More